Zhifang Liu

LLMs can drive significant gains in ad systems, not just as rankers or retrievers, but as complementary predictors that forecast likely advertisers from user profiles.

Forget end-to-end fine-tuning: $M^2$-VLA unlocks the power of generalized VLMs for robotic manipulation by intelligently mixing layers and incorporating meta-skills.

Forget fixed pipelines: training an agent to *learn* when and how to search for knowledge dramatically improves performance on knowledge-based visual question answering.